<p>The shortest path routing problem is a multi-objective optimization challenge that involves balancing often conflicting goals, such as minimizing transportation distance, cost, and time. To address such problems, hybrid methods using randomly generated weights have been employed to handle multiple objectives effectively. This research introduces a hybrid method, the self-organizing genetic algorithm (SOGA), enhanced by the integration of adaptive weight within its genetic algorithm loop and Dijkstra’'s algorithm in the initialization phase. Numerical experiments conducted on networks of up to 10,000 nodes demonstrate that the proposed path-based Dijkstra encoding and adaptive weight-enhanced SOGA outperform existing approaches in terms of robustness and computational efficiency. Compared to methods such as NSGA-II, SPEA, and traditional GA, the enhanced SOGA achieves a superior balance between solution quality and runtime performance. A case study on the East African Central Corridor further validates its practical applicability, achieving up to 16% cost savings and 20% distance reductions compared to benchmark solutions, albeit with slightly increased delivery times. Overall, this approach offers a promising framework for logistics planning, advancing the use of innovative, time-sensitive, and cost-effective routing strategies in multimodal transportation networks.</p>